gather with rtweet

license: “CC BY-NC”
Creative Commong: Attribution, Non-Commerical
https://creativecommons.org/licenses/by-nc/4.0/

Find this repository: https://github.com/libjohn/workshop_twitter_analysis

Much of this review comes from Introduction to gathering tweets with rwteet using the rtweet package. Conveniently, you no longer need a Twitter API developer key to use this package. You do need a Twitter account.

How to gather twitter data via the API

Load library packages

Use the tidyverse and rtweet

library(tidyverse)
library(rtweet)

Search tweets

search_tweets() - search a keyword(s) or hashtag

I recommend limiting the number of tweets returned (n = 1000) for this training. Otherwise you may hit a rate limit.

#bts <- search_tweets("#BTS", n = 5000, include_rts = FALSE)
bts_dynamite <- search_tweets("#BTS dynamite", n = 1000, include_rts = FALSE)

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Gathering tweets will return a 90 variable tibble of whatever row-size you were able to collect. You’ll want to spend some time familiarizing yourself with these variables and the range of data that can be gathered.

# bts
bts_dynamite

Serialize the gathering

• Windows: https://github.com/bnosac/taskscheduleR
• Linux: https://github.com/bnosac/cronR#cronr

get friends

Find all the accounts a user follows

john_little <-  get_friends("john_little")

This returns the twitter name, i.e. user, and the user_id for each person following that user.

john_little

Next, use lookup_users with the user_id to get more information about those accounts.

john_little_data <- lookup_users(john_little$user_id)

john_little_data

get followers

Who is following me? get_followers()

jrl_flw <- get_followers("john_little")

jrl_flw_data <- lookup_users(jrl_flw$user_id)

jrl_flw_data 

timelines

Get the most recent tweets from an account

rg_tmls <- get_timelines("RhiannonGiddens", n = 3200)

Visuzlize

rg_tmls %>% 
  dplyr::filter(created_at >= "2016-01-01") %>%  
  dplyr::group_by(screen_name) %>%
  ts_plot("weeks", trim = 1L) +
  ggplot2::geom_point() +
  geom_smooth(se = FALSE, color = "cadetblue") +
  colorblindr::scale_color_OkabeIto() +
  hrbrthemes::theme_ipsum(grid = "Y") +
  ggplot2::theme(
    legend.title = ggplot2::element_blank(),
    legend.position = "bottom", 
    plot.title = ggplot2::element_text(face = "bold")
    ) +
    ggplot2::labs(
    x = NULL, y = NULL,
    title = "Frequency of Twitter statuses",
    subtitle = "Twitter status (tweet) counts aggregated by week from Jan. 2016",
    caption = "Source: Data collected from Twitter's REST API via rtweet"
  )

NA
NA

get_favorites

Get the most recent favorites from a user

rg_faves <- get_favorites("RhiannonGiddens", n = 3000)

rg_faves

Profiles

Search a users’ profiles

gullah <- search_users("#gullah", n = 1000)

Searching for users...
Finished collecting users!

gullah

get trends

What is trendingin a specific location?

# sf <- get_trends("san franciso")
# durham <- get_trends(lat = 36.0, lng = -78.9)
greensboro <- get_trends("greensboro")

greensboro

Location information

Using the tidygeocoder R library, we can find location information when place_names are available.

First, geocode

Use the tidygeocoder package.

# glimpse(rg_tmls)
rg_places <- rg_tmls %>% 
  drop_na(place_name) %>% 
  select(place_name:bbox_coords) %>% 
  distinct() %>% 
  mutate(addr = glue::glue("{place_full_name}, {country}")) %>% 
  tidygeocoder::geocode(addr, method = "osm")

rg_places

Visuzlize

You can create maps in R. Below is one of the easiest methods, especially if you know ggplot2

rg_places %>% 
  distinct() %>% 
  drop_na(lat) %>% 
  ggplot(aes(long, lat), color="grey99") +
  borders("world") + 
  geom_point(color = "goldenrod") + 
  ggrepel::geom_label_repel(aes(label = place_full_name), 
                            segment.color = "goldenrod", segment.size = 1,
                            color = "navy") + 
  theme_void()

Second location example

Very similar to above. For accounts with “#gullah” in their profile, and that have location information listed, geocode the locations …..

gullah_places <- gullah %>% 
  drop_na(place_name) %>% 
  select(place_name:bbox_coords) %>% 
  filter(country_code == "US")  %>%
  distinct() %>% 
  mutate(addr = glue::glue("{place_full_name}, {country}")) %>% 
  tidygeocoder::geocode(addr, method = "osm")

gullah_places

And now visualize on a US map of the lower 48 states.

You can learn more about basic R mapping from our workshop on mapping with R

gullah_places %>% 
  distinct() %>% 
  drop_na(lat) %>% 
  ggplot(aes(long, lat), color="grey99") +
  borders("state") + 
  geom_point(color = "goldenrod") + 
  ggrepel::geom_label_repel(aes(label = place_full_name), 
                            segment.color = "goldenrod", segment.size = 1,
                            color = "navy") + 
  theme_void()